# The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

arXiv: https://arxiv.org/abs/1811.12535 · HTML: https://mlsanity.com/paper/1811.12535/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Deep Ensembles from a Bayesian Perspective](https://mlsanity.com/paper/2105.13283/) (2021)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis](https://mlsanity.com/paper/2406.06946/) (2024)
- [Bayesian Neural Networks: Essentials](https://mlsanity.com/paper/2106.13594/) (2021)
- [TapType: Ten-finger text entry on everyday surfaces via Bayesian inference](https://mlsanity.com/paper/2410.06001/) (2024)
- [Focused Active Learning for Histopathological Image Classification](https://mlsanity.com/paper/2404.04663/) (2024)
- [Bayesian Learning for Disparity Map Refinement for Semi-Dense Active Stereo Vision](https://mlsanity.com/paper/2209.05082/) (2022)
- [Information-Geometric Barycenters for Bayesian Federated Learning](https://mlsanity.com/paper/2412.11646/) (2024)
- [Model Architecture Adaption for Bayesian Neural Networks](https://mlsanity.com/paper/2202.04392/) (2022)
- [B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling](https://mlsanity.com/paper/2507.01714/) (2025)
- [Partial Trace-Class Bayesian Neural Networks](https://mlsanity.com/paper/2511.01628/) (2025)
- [Hidden Axis of Uncertainty: Latent-Posterior Alignment in Graph Neural Networks with Bayesian Output Layers](https://mlsanity.com/paper/2608.20758/) (2026)

## Abstract (arXiv)

One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian networks are needed to successfully capture model uncertainty. We vary the number and position of Bayesian layers in a network and compare their performance on active learning with the MNIST dataset. We found that we can fully capture the model uncertainty by using only a few Bayesian layers near the output of the network, combining the advantages of deterministic and Bayesian networks.